• ## Introduction to CFA Level I Formula Sheet Equations and Latex Code-QUANTITATIVE ECONOMICS and FINANCIAL REPORTING

rockingdingo 2024-01-30 #CFA #CFA I #AI Courses

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

• ## Latex for Equilibrium Chemical Equations and Formulas Latex

rockingdingo 2023-05-28 #Chemical #Chemistry

In this blog, we will summarize the latex code of most popular equations and formulas for Equilibrium, Chemistry. The topics include Acid Ionization Constant, Base Ionization Constant, Relationship of Conjugate Acidâ??Base Pair, Negative Logarithms Relationship of Conjugate Acidâ??base Pair, Buffer Design Equation, Gas Pressure and Concentration Relationship, Ion Product Constant for Water, pH and pOH Relationship, pH Definition, pOH Definition, pKa Definition, pKb Definition, pOH and Base Ionization Equilibrium Constant Relationship.

• ## Latex Code for MacroEconomics Formula and Equation

rockingdingo 2023-05-14 #Economics #MacroEconomics

In this blog, we will summarize the latex code of most popular formulas and equations for Economics-MacroEconomics. We will cover important topics, including Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Budget Deficit, Financial Account Balance, Consumer Price Index CPI, Consumption Function, Current-Account Balance, Equality of Leakages and Injections, Equation of Exchange, Gross Domestic Product GDP, Gross Domestic Product Deflator, Inflation Between Two Years, Merchandise Trade Balance, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate, etc.

• ## List of Complex Variables Formulas Latex Code

rockingdingo 2023-03-12 #Complex Variables #Conjugate #Power Series

In this blog, we will summarize the latex code for complex variables formulas, including complex numbers, De Moivreâ??s theorem and power series for complex variables e^{z}, sin(z), cos(z), ln(1+z), (1+z)^{n}, etc.

• ## Cheatsheet of Latex Code for Graph Neural Network(GNN) Equations

rockingdingo 2022-07-17 #graph neural network #gnn #gcn #gat #graphsage

In this blog, we will summarize the latex code of equations of Graph Neural Network(GNN) models, which are useful as quick reference for your research. For common notation, we denote G=(V,E) as the graph. V as the set of nodes with size |V|=N, and E as the set of N_e edges as |E| = N_e. A is denoted as the adjacency matrix. For each node v, we use h_v and o_v as hidde state and output vector of each node.